AIMC Topic: Protein Folding

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Protein structures for all.

Science (New York, N.Y.) Dec 16, 2021
AI-powered predictions reveal the shapes of proteins by the thousands.
Artificial Intelligence Models, Molecular Protein Conformation Protein Folding Software Proteins Databases, Protein
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Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the p...
Sequence Analysis, Protein Computational Biology Proteins Algorithms Protein Folding Deep Learning
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Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods ...
Machine Learning Sequence Alignment Algorithms Computational Biology Sequence Analysis, Protein Amino Acid Sequence Proteins Protein Folding Databases, Protein
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Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.

International journal of molecular sciences Nov 27, 2021
The field of protein structure prediction has recently been revolutionized through the introduction of deep learning. The current state-of-the-art tool AlphaFold2 can predict highly accurate structures; however, it has a prohibitively long inference ...
Neural Networks, Computer Computational Biology Proteins Humans Sequence Alignment Software Design Protein Structural Elements Amino Acid Sequence Protein Conformation Protein Folding
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The AlphaFold Database of Protein Structures: A Biologist's Guide.

Journal of molecular biology Oct 29, 2021
AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the...
Molecular Biology Computational Biology Proteome Models, Molecular Protein Conformation Protein Folding Algorithms Databases, Factual Deep Learning Humans
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Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment s...
Sequence Analysis, Protein Algorithms Computational Biology Support Vector Machine Proteins Pattern Recognition, Automated Protein Folding
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Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Nature communications Sep 27, 2021
Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversifi...
Datasets as Topic Glycosyltransferases Protein Folding Protein Structure, Secondary Deep Learning Sequence Alignment Glycosylation Protein Structure, Tertiary Computational Biology Databases, Genetic Amino Acid Sequence
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When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins Sep 23, 2021
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...
Neural Networks, Computer Software Reproducibility of Results Computational Biology Protein Folding Proteins Sequence Analysis, Protein Models, Molecular Structural Homology, Protein
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Improved 3-D Protein Structure Predictions using Deep ResNet Model.

The protein journal Sep 12, 2021
Protein Structure Prediction (PSP) is considered to be a complicated problem in computational biology. In spite of, the remarkable progress made by the co-evolution-based method in PSP, it is still a challenging and unresolved problem. Recently, alon...
Software Computational Biology Protein Folding Proteins Neural Networks, Computer Databases, Protein Models, Molecular Protein Conformation
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New Frontiers for Machine Learning in Protein Science.

Journal of molecular biology Sep 6, 2021
Protein function is fundamentally reliant on inter-molecular interactions that underpin the ability of proteins to form complexes driving biological processes in living cells. Increasingly, such interactions are recognised as being formed between pro...
Protein Folding Humans Machine Learning Proteins Animals Protein Aggregates Protein Interaction Maps Phase Transition
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